The Estimated Time-Varying Reproduction Numbers during the Ongoing Pandemic of the Coronavirus Disease 2019 (COVID-19) in 12 Selected Countries outside China

  1. Fu-Chang Hu

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Abstract

Background: How can we anticipate the progression of the ongoing pandemic of the coronavirus disease 2019 (COVID-19)? As a measure of transmissibility, we aimed to estimate concurrently the time-varying reproduction number, R0(t), over time during the COVID-19 pandemic for each of the following 12 heavily-attacked countries: Singapore, South Korea, Japan, Iran, Italy, Spain, Germany, France, Belgium, United Kingdom, the United States of America, and South Africa. Methods: We downloaded the publicly available COVID-19 pandemic data from the WHO COVID-19 Dashboard website (https://covid19.who.int/) for the duration of January 11, 2020 and May 1, 2020. Then, we specified two plausible distributions of serial interval to apply the novel estimation method implemented in the incidence and EpiEstim packages to the data of daily new confirmed cases for robustly estimating R0(t) in the R software. Results: We plotted the epidemic curves of daily new confirmed cases for the 12 selected countries. A clear peak of the epidemic curve appeared in 10 of the 12 selected countries at various time points, and then the epidemic curve declined gradually. However, the United States of America and South Africa happened to have two or more peaks and their epidemic curves either reached a plateau or still climbed up. Almost all curves of the estimated R0(t) monotonically went down to be less than or close to 1.0 up to April 30, 2020 except Singapore, South Korea, Japan, Iran, and South Africa, of which the curves surprisingly went up and down at various time periods during the COVID-19 pandemic. Finally, the United States of America and South Africa were the two countries with the approximate R0(t) ≥ 1.0 at the end of April, and thus they were now facing the harshest battles against the coronavirus among the 12 selected countries. By contrast, Spain, Germany, and France with smaller values of the estimated R0(t) were relatively better than the other 9 countries. Conclusion: Seeing the estimated R0(t) going downhill speedily is more informative than looking for the drops in the daily number of new confirmed cases during an ongoing epidemic of infectious disease. We urge public health authorities and scientists to estimate R0(t) routinely during an epidemic of infectious disease and to report R0(t) daily to the public until the end of the epidemic.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.10.20097154: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    As listed on the Comprehensive R Archive Network (CRAN) (https://cran.r-project.org/)
    https://cran.r-project.org/
    suggested: (CRAN, RRID:SCR_003005)
    The estimate_R function of the EpiEstim package assumes a Gamma distribution for SI by default to approximate the infectivity profile.
    EpiEstim
    suggested: (EpiEstim, RRID:SCR_018538)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    This study had several limitations because we relied on some assumptions to make a rapid analysis of this ongoing epidemic feasible. First, we assumed that all new cases of COVID-19 in each of the 12 selected countries are detected and reported to the WHO correctly. However, asymptomatic or mild cases of COVID-19 are likely undetected, and thus under-reported, especially in the early phase of the epidemic.5,14 In some countries, a lack of diagnostic test kits for the SARS-CoV-2 and a shortage of qualified manpower for fast testing can also cause under-reporting or delay in reporting. Nevertheless, we only analyze the official source of epidemic data for examining the trend of R0(t) during an epidemic because it is feasible, transparent, and reproducible. Unintentional under-reporting is inevitable in any country during an ongoing epidemic due to various reasons. Second, since the reported number of daily new confirmed cases includes both domestic and repatriated cases according to the description of WHO,4 the imported cases lead to an over-estimation of R0(t) in our epidemic analysis. Yet, they most likely appear in the early phase of the COVID-19 pandemic, and then have less effect on the estimated R0(t) in the later phase of the pandemic. The estimate_R function of the EpiEstim package provides the option for including the separated data of imported cases in the estimation of R0(t).7 Thus, our epidemic analysis can be easily refined once the domestic and repatriated cases a...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • No funding statement was detected.
    • No protocol registration statement was detected.

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  2. Version published to 10.1101/2020.05.10.20097154 on medRxiv